# GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction

arXiv: https://arxiv.org/abs/2601.13207 · HTML: https://mlsanity.com/paper/2601.13207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 26 of 26)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
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- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Revisiting IM2GPS in the Deep Learning Era](https://mlsanity.com/paper/1705.04838/) (2017)
- [Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence](https://mlsanity.com/paper/2212.04074/) (2022)
- [CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps](https://mlsanity.com/paper/1808.02130/) (2018)
- [GPT4GEO: How a Language Model Sees the World's Geography](https://mlsanity.com/paper/2306.00020/) (2023)
- [Where in the World is this Image? Transformer-based Geo-localization in the Wild](https://mlsanity.com/paper/2204.13861/) (2022)
- [PIGEON: Predicting Image Geolocations](https://mlsanity.com/paper/2307.05845/) (2023)
- [Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes](https://mlsanity.com/paper/2303.04249/) (2023)
- [Deep Outdoor Illumination Estimation](https://mlsanity.com/paper/1611.06403/) (2016)
- [Are Large Language Models Geospatially Knowledgeable?](https://mlsanity.com/paper/2310.13002/) (2023)
- [LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild](https://mlsanity.com/paper/2405.20363/) (2024)
- [Granular Privacy Control for Geolocation with Vision Language Models](https://mlsanity.com/paper/2407.04952/) (2024)
- [Learning a Dynamic Map of Visual Appearance](https://mlsanity.com/paper/2012.14885/) (2020)
- [Image-Based Geolocation Using Large Vision-Language Models](https://mlsanity.com/paper/2408.09474/) (2024)
- [GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning](https://mlsanity.com/paper/2506.00785/) (2025)
- [AI Sees Your Location, But With A Bias Toward The Wealthy World](https://mlsanity.com/paper/2502.11163/) (2025)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space](https://mlsanity.com/paper/2507.10473/) (2025)
- [Learning Geo-Temporal Image Features](https://mlsanity.com/paper/1909.07499/) (2019)
- [Just Noticeable Difference for Large Multimodal Models](https://mlsanity.com/paper/2507.00490/) (2025)
- [Content-Aware Detection of Temporal Metadata Manipulation](https://mlsanity.com/paper/2103.04736/) (2021)

## Abstract (arXiv)

Geo-localization aims to infer the geographic location where an image was captured using observable visual evidence. Traditional methods achieve impressive results through large-scale training on massive image corpora. With the emergence of multi-modal large language models (MLLMs), recent studies have explored their applications in geo-localization, benefiting from improved accuracy and interpretability. However, existing benchmarks largely ignore the temporal information inherent in images, which can further constrain the location. To bridge this gap, we introduce GTPred, a novel benchmark for geo-temporal prediction. GTPred comprises 370 globally distributed images spanning over 120 years. We evaluate MLLM predictions by jointly considering year and hierarchical location sequence matching, and further assess intermediate reasoning chains using meticulously annotated ground-truth reasoning processes. Experiments on 8 proprietary and 7 open-source MLLMs show that, despite strong visual perception, current models remain limited in world knowledge and geo-temporal reasoning. Results also demonstrate that incorporating temporal information significantly enhances location inference performance.